International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Goal-oriented Movement Reality-based Skeleton Animation Using Machine Learning

  • Yu-Won JEONG (College of IT Engineering, Dept. of Media Software, Sungkyul University)
  • Received : 2024.04.18
  • Accepted : 2024.05.01
  • Published : 2024.05.31
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Abstract

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.

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References

  1. Global Game Industry Trends, July/August 2023, Issue No. 60, 2023.
  2. S. D. Yoo, "Governance research for Artificial intelligence service", The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 24, No. 2, pp.15-21, Apr 30, 2024. DOI: https://doi.org/10.7236/JIIBC.2024.24.2.15
  3. H. W. Jo and H. W. Jung, "Design and Implementation of RPG Auto Play System using Reinforcement Learning", Journal of the Korea Entertainment Industry Association, 17(4), pp. 351-359, 2023. DOI: 10.21184/jkeia.2023.6.17.4.351
  4. Y.H. Lee, "Implementation of Intelligent Agent Based on Reinforcement Learning Using Unity ML-Agents", The Journal of JIIBC, Volume 24 Issue 2, pp. 205-211, 2024. DOI: https://doi.org/10.7236/JIIBC.2024.24.2.205
  5. D. S. Lim, Y. A. Min and D. K. Lim, "Recommendation System of University Major Subject based on Deep Reinforcement Learning", The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 23, No. 4, pp.9-15, Aug 31, 2023. DOI: https://doi.org/10.7236/JIIBC.2023.23.4.9
  6. C. S. Kim, N. G. Kim and K. K. Young, "Research Trends Analysis of Machine Learning and Deep Learning: Focused on the Topic Modeling", Journal of the Korea Society of Digital Industry and Information Management, 15(2), pp.19-28, 2019. DOI: https://doi.org/10.17662/ksdim.2019.15.2.019
  7. S. C Park, D. Y. Kim and W. J. Lee, "UnityPGTA: A Unity Plat former Game Testing Automation Tool Using Reinforcement Learning", Journal of KIISE, 51(2), pp.149-156, 2024. DOI: 10.5626/JOK.2024.51.2.149
  8. Unity-Technologies, ml-agents, https://github.com/Unity-Technologies/ml-agents
  9. D. E. Park, "Design and Implementation of Puzzle Game Play Agent Using PPO Algorithm", Journal of JCIT, vol.11, no.3, pp. 1-6, 2021. DOI : 10.22156/CS4SMB.2021.11.03.001
  10. S. G. Park and D. H. Kim, "Autonomous Flying of Drone Based on PPO Reinforcement Learning Algorithm", Journal of Institute of Control, Robotics and Systems, 26(11), pp. 955-963, 2020. DOI: 10.5302/J.ICROS.2020.20.0125
  11. S. G. Lee, K. S. Byun and H. J. Yoon, "Adaptive Fast Calibration Method for Active Phased Array Antennas using PPO Algorithm", Journal of IKEEE, 27(4), pp. 269-276, 2023. DOI: https://doi.org/10.7471/ikeee.2023.27.4.636